Publication of quality improvement in medical oncology: A descriptive numerical summary from a scoping review.
Bibliographic record
Abstract
251 Background: Quality improvement (QI) is becoming a professional expectation and essential to medical oncology practice. A study of medical oncologists found that most QI interventions are not published, suggesting barriers to knowledge dissemination. We aim to describe the authors, settings of QI interventions, and publishing journals of scholarly QI literature. Methods: We conducted a scoping review using Arksey and O'Malley's framework. A search of MEDLINE and EMBASE databases found 48,186 unique English citations from January 2001 to August 2014. Two independent reviewers were responsible for screening the search results and 270 studies were included. Characteristics of first authors, settings of QI interventions, and publishing journals were charted. We used online search engines to find institutional profiles to obtain author and institutional information. A descriptive numerical summary analysis was used to summarize and report the results. Results: The number of QI publications has increased over time, with 60 between 2001 and 2006 and 199 from 2007 to 2013. The majority of first authors are clinicians (65%), of which 59% are physicians, 31% are nurses, and 5% are pharmacists. Furthermore, 27% of first authors are primarily researchers whereas 5% are solely administrators. In addition to professional degrees, having an advanced degree was common amongst clinicians (48% of physicians, 85% of nurses, 44% of pharmacists). Forty-four percent of interventions were conducted in settings affiliated with an academic institution, as opposed to community-based settings. Only 9% of articles were published in a quality of care focused journal. Conclusions: Our scoping review found that most first authors of QI interventions are clinicians, many with an advanced degree in academic settings (rather than community-based settings where most patients receive care). The lack of studies published in quality of care focused journals may result in lost opportunities for knowledge transfer. These findings suggest that more effective knowledge dissemination and increased support for QI studies are needed to further the science of quality and ultimately improve quality of care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.092 | 0.103 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".